Profile likelihood estimation of partially linear panel data models with fixed effects
By Liangjun Su, Aman Ullah. 2006
137 citations recorded by the source corpus.
Abstract We consider consistent estimation of partially linear panel data models with fixed effects. We propose profile-likelihood-based estimators for both the parametric and nonparametric components in the models and establish convergence rates and asymptotic normality for both estimators.
Subfields: Economics and Econometrics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/3139944f108e7368eabca2ea4fb62c634af53b1b
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Citations in the corpus
- Testing for Common Trends in Semiparametric Panel Data Models with Fixed Effects, forthcoming (2012, methodology): “Following Su and Ullah (2006) and CGL (2010), we estimate the model (2.”
- Semiparametric GMM estimation of spatial autoregressive models (2012, background): “It is worth mentioning that the idea of nonparametric profiling is not new in the literature; see Su and Ullah (2006) and Henderson et al. (2008) for recent applications in econometrics.”
- A consistent nonparametric test of parametric regression functional form in fixed effects panel data models (2014, methodology): “Su and Ullah (2006) used the same D matrix and a different estimation procedure to estimate a partially linear fixed-effects panel data model. we show that our proposed estimator θ̂(x) = n−1 ∑n i=1 ∑T t=1 ωitYit, where ωit = λit/ ∑T s=1 λis.”
- Spline-backfitted kernel smoothing of partially linear additive model (2011, unknown): “For applications of partially linear model to panel data, see Su and Ullah (2006), while for data with measurement errors, see Liang et al. (2007, 2008).”
- Semiparametric Trending Panel Data Models with Cross-Sectional Dependence (2012, methodology): “As both T and N tend to infinity jointly, the asymptotic variance in (3.7) is simplified, compared with some existing literature on the profile likelihood estimation of semiparametric panel data models with fixed effects (see, for example, Su and Ullah 2006).”
- Co-Benefits and Additionality of the Clean Development Mechanism: An Empirical Analysis (2011, unknown): “With this model (12) can be converted to a nonparametric fixed effect regressionˆy it = m(w it ) + u i + ε it , (15) Multiple methods are available to estimate this model including the series method and the profile-kernel method (Lin and Ying, 2001; Su and Ullah, 2006).”
- Sovereign bond yield spreads: A time-varying coefficient approach (2012, unknown): “For identification purpose, we impose similar to Su and Ullah (2006), ∑N i=1 μi = 0, such that D = [−eN−1 IN−1] ′ ⊗ eT is an (NT ) × (N − 1) matrix, where IN−1 denotes an identity matrix of dimension N − 1 and eN−1 a (N − 1)× 1 vector with all elements being 1.”
- Nonparametric dynamic panel data models: Kernel estimation and specification testing (2013, background): “19) has the structure of moving averaging of order 1 (MA(1)), one may be tempted to explore such an MA(1) structure to obtain a more efficient estimate of m (u) , say, by following the lead of Xiao, Linton, Carroll and Mammen (2003) and Su and Ullah (2006b). Unfortunately, the latter papers require ”
- Estimating Semiparametric Panel Data Models by Marginal Integration (2012, unknown): “Su and Ullah (2006) propose to estimate the nonlinear component by profile likelihood estimation, which boils down to a locally linear kernel smoothing, controlling for fixed effects by dummies.”
- Estimation in Partially Linear Single-Index Panel Data Models With Fixed Effects (2013, methodology): “When Xit are scalar, model (1.1) becomes a partially linear panel data model with fixed effects (Su and Ullah 2006).”